David de la Iglesia Castro

> I recently discovered the experimental 'Checkpointing IO API' for Pytorch Lightning: > > ```python > import torch, os > > from pytorch_lightning import Trainer > from pytorch_lightning.plugins import CheckpointIO...

Another option would be to try to maintain integrations compatible across different versions by adding conditionals in our code. For example (#219): ```python if self.model_file: if catalyst.version = 0.22.0 ....

We should get rid of integrations and transfer to the framework itself when possible. As mentioned in the first point of #223

> After #220 we realized that there is no easy way to maintain different integrations - on one hand, `dvclive` is not that important to make users specific version of...

Thanks! It's a good idea

I am considering to work on the integration with `pytorch-lightning` but I'm not sure about where to contribute the new logger (i.e. this repository or `pytorch-lightning` itself). See https://github.com/iterative/dvclive/issues/70#issuecomment-811868255

I added an integration with [mmcv](https://github.com/open-mmlab/mmcv): https://github.com/open-mmlab/mmcv/pull/1075

> @daavoo Thats a great news! Can we do something to help with that pull request? It has been already approved so I think it will be merged soon, thanks!

I think it might be a good idea to have separated issues for each integration in order to better track the progress and have specific discussions for each one (i.e....

Reviving this as I think that `skearn` should be the entry point for discussing what can `dvclive` provide in "stepless" scenarios (no deep learning no gradient boosting) beyond https://github.com/iterative/dvclive/issues/182 Taking...